The more technology evolves, the easier data collection becomes. Businesses are collecting data in greater amounts and at faster rates than ever before, with 53% of businesses adopting big data analytics. This makes it quite the challenge not just for those developing data storage capabilities, but more so for those who have to make sense of it all.
It falls to these big data specialists to interpret data and put it into forms that businesses can understand and use to hone their competitive edge. Because there is so much data, and not enough specialists to leverage its potential, data analytics courses in Singapore with SkillsFuture funding abound, and continue to increase in popularity.
These courses prepare big data or analytics specialists for careers in industries such as
- Manufacturing
- Retail
- Banking
- Services
- Logistics
- Law Enforcement
- Disaster Management
- Healthcare
- Education
- Government
If you see yourself pursuing such a career, you’ll have to know exactly what big data is and how businesses can use it, and learn the necessary skills for helping these businesses out.
What’s the Big Deal About Big Data?
Big data is defined according to the source it is collected from, whether it’s from machines or the equipment being used in a business’ operations, transactions with suppliers or customers, or even from social media and other digital platforms. There are also government and other sources of information accessible by the public.
What differentiates big data from “just data” is the Volume, Velocity, and the Variety or the different formats in which it is gathered—these are the “three V’s” from Gartner analyst Doug Laney’s definition. The Value of the data, as well as how it may be Verified, are two more V’s that have since been added to the popular definition.
Because it’s not so much about what big data is than the possibilities it provides businesses for improving efficiency and profitability. If a business knows how to use its data correctly, it will be able to
- Find ways to save time and cut down on operational expenses
- Detect and suggest solutions for operational anomalies (including fraud)
- Facilitate predictive maintenance processes
- Make forecasts and improve risk management
- Develop new or enhance existing products and services
- Enable machine learning (instead of “merely programming”)
- Make better-informed strategic decisions
This ability to use data relies entirely on how well specialists are able to process, analyse and present it to stakeholders, enabling everyone to gain insights as well as to suggest and take courses of action. The “bigness” of big data also demands these specialists to be discerning as to which specific sets of data should be collected and subsequently used.
Preparing for a Big-Time Career in Big Data
As with any “big” career, it makes sense to “start out small”—a career in IT generally has its foundations in a bachelor’s degree in the likes of computer science or computer engineering. If you already have a degree in another field and are thinking of shifting careers, a maths-related degree such as one in finance or statistics might help make the shift easier.
To become a big data or analytics specialist, a maths and computer background will definitely be needed. While a master’s degree or a PhD. in data science isn’t required, it could make a difference if you’re eyeing a management position when the time comes for job-hunting. Work experience that involves handling data—such as setting up databases or programming—can also be a plus.
Specific skills you’ll need to develop include knowing how to manage database systems and frameworks which are essential in processing and streaming big data. Mastery of linear algebra and statistics, particularly probabilities, variables and testing of hypotheses are also must-haves for big data specialists.
But arguably the most important skills that you will need in a big data career are coding and programming—after all, you can’t expect to advance in an industry where you don’t “speak the language”. As technology continues to evolve, big data processing will continually have to be customised alongside it, which is where your knowledge of programming languages steps in.
R You Ready to Get Started?
There are many programming languages used in big data analytics, but R is one of the languages, if not the language a certified specialist needs to know. As the analytics and graphics software of choice, R is preferred by 40% of data scientists for its ability to facilitate analysis and make effective presentations.
R is highly adaptable in that statistical functionalities can be added and more data points can be handled as needed. Coding in R is also comparatively easy, in that coding can be written in cloud-based parallels without having to purchase high-end computers. R can easily handle large sets of data, and is readily incorporated with online platforms to create apps for analytics.
Using R offers several advantages, such as its widespread use among researchers and analysts; its straightforwardness as a programming language, and its comprehensive array of tools for manipulating and wrangling data. On top of making data easy to visualise and analyse, R also makes it easy for new statistical methods to be used, and machine learning to be implemented.
R is also open-source or free to use, making it cost-efficient, widely accessible, and highly scalable on an individual, organisational and industrial level.
Now while R should be relatively simple to learn for those familiar with popular spreadsheet programs, how quickly you pick it up depends on your prior mastery of programming and statistics. Other challenges involved in learning R include its huge computer memory requirements and having to make copies of the data being processed.
The Certified Data Analytics (R) Specialist Course
To help you overcome these challenges, SMU Academy offers hands-on opportunities for sharpening your R skills along with real-world data-sets and cases. The Academy’s Certified Data Analytics (R) Specialist course comprises six modules and a capstone, practical problem-solving project, completion of which is required to earn your certificate.
Course modules include introductions to Data Analytics and Data Visualisation, Statistical Inference for Managerial Insights, Advancing Skillsets of Visual Analytics, Web Scraping and Data Insights, and A First Look at Visual Analytics.
But even as you master the intricacies of using R in becoming a big data specialist, bear in mind that your work—your findings, analyses and presentations—must be aligned with the overall business goals of your organisation. In analytics, data is not collected aimlessly or for its own sake, but is called to serve a greater purpose which, simply put, is to find ways to make things better for a business and the people it serves.
In other words, to become a big data specialist, you must never lose sight of the big picture.
